System and Method for Building a Predictive Score Without Model Training

1. A computer-implemented method of building a predictive score without model training, the method comprising: defining, by a computer, a set of predictive variables based on raw data fields generated from raw data from one or more sources, the raw data including a historical set of transactions previously generated by one or more raw data sources; generating, by the computer, a relative risk table to describe each predictive variable of the set of predictive variables; adapting, by the computer, each predictive variable to an average value of one; and combining, by the computer, the set of predictive variables having the average value of one using their associated relative risk tables to generate a predictive score for a future set of transactions; wherein each predictive variable is assigned an adapted relative risk R, and wherein for n rescaled relative-risks: R 1 , R 2 . . . Rn the eredictive score S can be generated as: S = a ⁡ ( R 1 × R 2 × … × R n n ) b 1 + a ⁡ ( R 1 × R 2 × … × R n n ) b where a and b are constants >0 to control a calibration of the predictive score S.

2. The method in accordance with claim 1 , wherein the raw data includes domain knowledge of the set of transactions.

3. A computer-implemented method of building a predictive score without model training, the method comprising: accessing, by a computer, raw data from one or more sources, the raw data including a historical set of transactions previously generated by one or more raw data sources; defining raw data fields from the raw data; defining a set of predictive variables based on the raw data fields generated from raw data from one or more raw data sources; generating, by the computer, a relative risk table to describe each predictive variable of the set of predictive variables; adapting each predictive variable to an average value of one; combining the set of predictive variables having the average value of one using their associated relative risk tables; and generating, by the computer, a predictive score for a future set of transactions according to the combined set of predictive variables; wherein each predictive variable is assigned an adapted relative risk R, and wherein for n relative-risks: R 1 , R 2 . . . Rn the predictive score S can be generated as: S = a ⁡ ( R 1 × R 2 × … × R n n ) b 1 + a ⁡ ( R 1 × R 2 × … × R n n ) b where a and b are constants >0 to control a calibration of the predictive score S.

4. The method in accordance with claim 3 , wherein the raw data includes domain knowledge of the set of transactions.

5. A system for building a predictive score without model training, the system comprising: a computing system including a processor for executing instructions encoded in a tangible medium, the instructions comprising: a data fields definition module for defining a set of predictive variables based on raw data fields generated from raw data from one or more raw data sources, a relative risk table generation module for generating a relative risk table to describe each predictive variable of the set of predictive variables; an average conversion module for adapting each predictive variable to an average value of one; a variable combination and score generation module for combining the set of predictive variables having the average value of one using their associated relative risk tables, and for generating a predictive score for a future set of transactions; wherein the relative risk table generation module is configured to assign each predictive variable an adapted relative risk R, and wherein for n relative-risks: R 1 , R 2 . . . Rn the predictive score Scan be generated as: S = a ⁡ ( R 1 × R 2 × … × R n n ) b 1 + a ⁡ ( R 1 × R 2 × … × R n n ) b where a and b are constants >0 to control a calibration of the predictive score S.

6. The system in accordance with claim 5 , wherein the raw data includes domain knowledge of the set of transactions.

7. The system in accordance with claim 5 , further comprising a communications network connected between the computing system and the one or more raw data sources.

8. The system in accordance with claim 5 , wherein the communications network includes an extranet.